In many manufacturing sectors, the market and efficiency demand that a single manufacturing facility produce a variety of goods or products, not just a single product variety. This manufacturing environment becomes more challenging when competitive pressure requires manufacturers to constantly improve/increase productivity in the manufacture of goods or products while at the same time decrease manufacturing costs and increase profits. In many cases, achieving the goals of (1) increased and effective manufacture of goods or products using a single manufacturing or production line and (2) meeting the competitive pressures are viewed as conflicting goals.
If a manufacturer is manufacturing goods or products that are to be produced in a number of varieties, it typically will require a large number of changeovers in production lines used to produce such goods or products. These changeover periods result in downtime for the machines used in the production line(s), which reduces productivity. In the face of the circumstances just described, manufacturers have tried various methods to attempt to optimize manufacturing and production processes by minimizing the number of changeovers required to manufacture or produce a variety of goods or products on a single production line.
In the manufacturing industry, the optimization process has been referred to as “production planning.” Production planning may be very complex. Part of the reason for this is the constraints that exist for any manufacturing facility. Further, each manufacturing facility has its own set of unique constraints or restrictions.
Currently, factory staff solves the production planning problem either “manually” or with the help of “rules-based” software systems. The manual process involves a factory employee who considers the current demand, the current state of the factory, and previous experiences gained during the operation of the factory to guess an optimal production sequence. The “rules-based” software system codifies the experiences of factory staff into rules and uses these rules for production planning. Therefore, a “rules-based” system, which may be computer-based, solves the problem in a way similar to the factory staff's method of solving the problem manually. Using “rules-based” systems, however, may speed up the process.
Examples of production planning situations are set forth in the following two scenarios. These scenarios are meant to be examples only and do not encompass every possible scenario that could affect the production planning sequences for these production situations.
Scenario 1: If a manufacturer is producing a particular product on a production line and the variety of this product based on demand that must be produced are three red products and three green products, the following will apply for coloring step of the process for these products. The color will be applied by a coloring device. This coloring device must be cleaned for 30 minutes before a new color can be applied. The most optimal way to manufacture the products having these two colors is to manufacture all of the products of one color first—for example, the red products—then changeover for the coloring device for the minute changeover period, followed by coloring the remaining products with the second color—for example, green. As noted, this production sequence has one downtime of 30 minutes to clean the coloring device
Scenario 2: Scenario 1 is a single changeover situation; however, in many cases the manufacturing process may be far more complicated for producing a variety of products. If the conditions for producing finished products, for example, require that partially finished products be transported to a different factory in special transportation crates for completion and there is a limited supply of these crates, and the shape of the product dictates the crate that is to be used for transportation, it is readily seen that the manufacturing process is more complicated. To illustrate the complications of the manufacturing process just described, if the product mix includes two products of the red variety and one product of the green variety and these have shape A, and all remaining products being manufactured have shape B, and all of the crates for shipping products with shape B are currently in use, then any products being produced that have shape B cannot be processed. The constraint associated with the availability of shipping crates can have a large impact on the manufacturing process.
Given these conditions, it is still necessary to determine a production schedule to optimize the use of the production line. The production schedule described for Scenario 1, which was to manufacture the reds first, changeover, and then manufacture the greens, will not work in this case because the third red product that has a shape B will require a transportation crate that is not available. Therefore, the production schedule must not only take into account the color but also the availability of the transportation crates (a factory restriction).
In the past, production planning has been principally solved by having knowledge of the factory restrictions through a set of production rules. These rules represented the know-how that factory workers developed over time in performing factory operations. For example, in Scenario 1, one such production rule could be stated as: “Produce the same colors in sequence.” In Scenario 2, the production rules could be stated as: “Produce same colors as long as transportation crates are available.” The problem that exists with relying on such a rule-based approached is that as soon as there is a change in any factory restrictions, there must be some corresponding change to the rules.
An example of how this may take place is the following. If the two Scenarios described above were altered with upgraded coloring equipment for the production line so that there was an additional “stand-by” coloring device, it would significantly change the production scheduling. This second coloring device could be used while the first device was being cleaned. More specifically, when a color change was necessary for the production of a variety of products, the second coloring machine can be brought on-line in the production sequence, while the first device is moved off-line to be cleaned. Thus, the changeover time for color changes would be reduced to zero from 30 minutes. The introduction of the second “stand-by” coloring machine would significantly change optimization of production scheduling.
Given this simple change in the production, any of the previous rules for optimizing productivity by minimizing color changeover times would become invalid and no longer needed; and the entire rule-based optimization scheme would have to be redesigned. As such, it is seen that rules-based systems and methods are fragile under changes in factory restrictions. Furthermore, rule-based optimization strategies also are only as good as the rules system, which is transitory, i.e., the knowledge of individuals, only sometimes experts, who derived the rules.
The present invention provides a system and method for optimization of production planning and overcomes these problems of the past based on manual or rule-based production planning.